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      "source": [
        "# Evaluation of a QA System\n",
        "\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial5_Evaluation.ipynb)\n",
        "\n",
        "To be able to make a statement about the performance of a question-answering system, it is important to evalute it. Furthermore, evaluation allows to determine which parts of the system can be improved."
      ],
      "metadata": {
        "collapsed": true,
        "id": "MGSXn0USOhtu",
        "pycharm": {
          "name": "#%% md\n"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Prepare environment\n",
        "\n",
        "#### Colab: Enable the GPU runtime\n",
        "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
        "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/master/docs/_src/img/colab_gpu_runtime.jpg\">"
      ],
      "metadata": {
        "collapsed": false,
        "id": "lEKOjCS5U7so"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "source": [
        "# Make sure you have a GPU running\n",
        "!nvidia-smi"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mon Oct 25 12:02:00 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 470.74       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   35C    P8    27W / 149W |      0MiB / 11441MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
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        "pycharm": {
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        },
        "id": "xhFIMX_7U7ss",
        "outputId": "285b2491-01e5-4bfd-cba9-c2279d4417c4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Start an Elasticsearch server\n",
        "You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source."
      ],
      "metadata": {
        "id": "E6H_7lAmOht8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "source": [
        "# Install the latest release of Haystack in your own environment \n",
        "#! pip install farm-haystack\n",
        "\n",
        "# Install the latest master of Haystack\n",
        "!pip install grpcio-tools==1.34.1\n",
        "!pip install git+https://github.com/deepset-ai/haystack.git\n",
        "\n",
        "# If you run this notebook on Google Colab, you might need to\n",
        "# restart the runtime after installing haystack."
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            "Building wheels for collected packages: farm-haystack, pytesseract, alembic, databricks-cli, sentence-transformers, gpustat, langdetect, python-docx, python-multipart, seqeval, tika\n",
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            "Successfully built farm-haystack pytesseract alembic databricks-cli sentence-transformers gpustat langdetect python-docx python-multipart seqeval tika\n",
            "Installing collected packages: multidict, yarl, sniffio, smmap, isodate, async-timeout, websocket-client, tokenizers, sacremoses, python-editor, platformdirs, pillow, opencensus-context, mypy-extensions, msrest, marshmallow, Mako, huggingface-hub, grpcio, gitdb, distlib, cryptography, blessings, backports.entry-points-selectable, azure-core, anyio, aiohttp, virtualenv, validators, ujson, typing-inspect, transformers, starlette, sentencepiece, redis, rdflib, querystring-parser, pydantic, py-spy, prometheus-flask-exporter, pluggy, opencensus, num2words, marshmallow-enum, h11, gunicorn, gpustat, gitpython, docker, databricks-cli, colorama, azure-storage-blob, asgiref, alembic, aioredis, aiohttp-cors, weaviate-client, uvloop, uvicorn, tox, tika, sqlalchemy-utils, SPARQLWrapper, seqeval, sentence-transformers, ray, quantulum3, python-multipart, python-docx, pytesseract, pymilvus, psycopg2-binary, pdf2image, mmh3, mlflow, langdetect, httptools, fastapi, faiss-cpu, elasticsearch, elastic-apm, dataclasses-json, farm-haystack\n",
            "  Attempting uninstall: pillow\n",
            "    Found existing installation: Pillow 7.1.2\n",
            "    Uninstalling Pillow-7.1.2:\n",
            "      Successfully uninstalled Pillow-7.1.2\n",
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            "    Found existing installation: grpcio 1.41.0\n",
            "    Uninstalling grpcio-1.41.0:\n",
            "      Successfully uninstalled grpcio-1.41.0\n",
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            "    Found existing installation: pluggy 0.7.1\n",
            "    Uninstalling pluggy-0.7.1:\n",
            "      Successfully uninstalled pluggy-0.7.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "pytest 3.6.4 requires pluggy<0.8,>=0.5, but you have pluggy 1.0.0 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed Mako-1.1.5 SPARQLWrapper-1.8.5 aiohttp-3.7.4.post0 aiohttp-cors-0.7.0 aioredis-2.0.0 alembic-1.4.1 anyio-3.3.4 asgiref-3.4.1 async-timeout-3.0.1 azure-core-1.19.0 azure-storage-blob-12.9.0 backports.entry-points-selectable-1.1.0 blessings-1.7 colorama-0.4.4 cryptography-35.0.0 databricks-cli-0.16.2 dataclasses-json-0.5.6 distlib-0.3.3 docker-5.0.3 elastic-apm-6.6.0 elasticsearch-7.10.0 faiss-cpu-1.7.1.post2 farm-haystack-1.0.0rc1 fastapi-0.70.0 gitdb-4.0.9 gitpython-3.1.24 gpustat-0.6.0 grpcio-1.37.1 gunicorn-20.1.0 h11-0.12.0 httptools-0.3.0 huggingface-hub-0.0.8 isodate-0.6.0 langdetect-1.0.9 marshmallow-3.14.0 marshmallow-enum-1.5.1 mlflow-1.13.1 mmh3-3.0.0 msrest-0.6.21 multidict-5.2.0 mypy-extensions-0.4.3 num2words-0.5.10 opencensus-0.8.0 opencensus-context-0.1.2 pdf2image-1.14.0 pillow-8.3.2 platformdirs-2.4.0 pluggy-1.0.0 prometheus-flask-exporter-0.18.4 psycopg2-binary-2.9.1 py-spy-0.3.10 pydantic-1.8.2 pymilvus-1.1.2 pytesseract-0.3.7 python-docx-0.8.11 python-editor-1.0.4 python-multipart-0.0.5 quantulum3-0.7.9 querystring-parser-1.2.4 ray-1.5.0 rdflib-6.0.2 redis-3.5.3 sacremoses-0.0.46 sentence-transformers-2.1.0 sentencepiece-0.1.96 seqeval-1.2.2 smmap-5.0.0 sniffio-1.2.0 sqlalchemy-utils-0.37.9 starlette-0.16.0 tika-1.24 tokenizers-0.10.3 tox-3.24.4 transformers-4.7.0 typing-inspect-0.7.1 ujson-4.2.0 uvicorn-0.15.0 uvloop-0.14.0 validators-0.18.2 virtualenv-20.9.0 weaviate-client-2.5.0 websocket-client-1.2.1 yarl-1.7.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "PIL"
                ]
              }
            }
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "vgmFOp82Oht_",
        "pycharm": {
          "name": "#%%\n"
        },
        "outputId": "5bbcbb42-3a90-43a9-ebfd-598a98fa7143"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "source": [
        "# In Colab / No Docker environments: Start Elasticsearch from source\n",
        "! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q\n",
        "! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz\n",
        "! chown -R daemon:daemon elasticsearch-7.9.2\n",
        "\n",
        "import os\n",
        "from subprocess import Popen, PIPE, STDOUT\n",
        "es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],\n",
        "                   stdout=PIPE, stderr=STDOUT,\n",
        "                   preexec_fn=lambda: os.setuid(1)  # as daemon\n",
        "                  )\n",
        "# wait until ES has started\n",
        "! sleep 30"
      ],
      "outputs": [],
      "metadata": {
        "id": "tNoaWcDKOhuL",
        "pycharm": {
          "is_executing": true,
          "name": "#%%\n"
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "source": [
        "from haystack.modeling.utils import initialize_device_settings\n",
        "\n",
        "devices, n_gpu = initialize_device_settings(use_cuda=True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ray/autoscaler/_private/cli_logger.py:61: FutureWarning: Not all Ray CLI dependencies were found. In Ray 1.4+, the Ray CLI, autoscaler, and dashboard will only be usable via `pip install 'ray[default]'`. Please update your install command.\n",
            "  \"update your install command.\", FutureWarning)\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w0MHgxrYOhur",
        "pycharm": {
          "name": "#%%\n"
        },
        "outputId": "b1db4179-b278-4e42-f326-509292b94fbd"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "source": [
        "from haystack.utils import fetch_archive_from_http\n",
        "\n",
        "# Download evaluation data, which is a subset of Natural Questions development set containing 50 documents\n",
        "doc_dir = \"../data/nq\"\n",
        "s3_url = \"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/nq_dev_subset_v2.json.zip\"\n",
        "fetch_archive_from_http(url=s3_url, output_dir=doc_dir)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tTXxr6TAOhuz",
        "pycharm": {
          "name": "#%%\n"
        },
        "outputId": "586d4775-4354-4ed9-a72c-c30bedcdfbee"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "source": [
        "# make sure these indices do not collide with existing ones, the indices will be wiped clean before data is inserted\n",
        "doc_index = \"tutorial5_docs\"\n",
        "label_index = \"tutorial5_labels\""
      ],
      "outputs": [],
      "metadata": {
        "id": "T-G7Ay2jU7s_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "source": [
        "# Connect to Elasticsearch\n",
        "from haystack.document_stores import ElasticsearchDocumentStore\n",
        "\n",
        "# Connect to Elasticsearch\n",
        "document_store = ElasticsearchDocumentStore(host=\"localhost\", username=\"\", password=\"\", index=doc_index,\n",
        "                                            label_index=label_index, embedding_field=\"emb\",\n",
        "                                            embedding_dim=768, excluded_meta_data=[\"emb\"])"
      ],
      "outputs": [],
      "metadata": {
        "id": "B_NEtezLOhu5",
        "pycharm": {
          "name": "#%%\n"
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "source": [
        "from haystack.nodes import PreProcessor\n",
        "\n",
        "# Add evaluation data to Elasticsearch Document Store\n",
        "# We first delete the custom tutorial indices to not have duplicate elements\n",
        "# and also split our documents into shorter passages using the PreProcessor\n",
        "preprocessor = PreProcessor(\n",
        "    split_length=200,\n",
        "    split_overlap=0,\n",
        "    split_respect_sentence_boundary=False,\n",
        "    clean_empty_lines=False,\n",
        "    clean_whitespace=False\n",
        ")\n",
        "document_store.delete_documents(index=doc_index)\n",
        "document_store.delete_documents(index=label_index)\n",
        "document_store.add_eval_data(\n",
        "    filename=\"../data/nq/nq_dev_subset_v2.json\",\n",
        "    doc_index=doc_index,\n",
        "    label_index=label_index,\n",
        "    preprocessor=preprocessor\n",
        ")\n",
        "\n",
        "# Let's prepare the labels that we need for the retriever and the reader\n",
        "labels = document_store.get_all_labels_aggregated(index=label_index, drop_negative_labels=True, drop_no_answers=False)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bRFsQUAJOhu_",
        "pycharm": {
          "name": "#%%\n"
        },
        "outputId": "477031b9-5c2c-4128-ef5f-54db86259734"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Initialize components of QA-System"
      ],
      "metadata": {
        "id": "gy8YwmSYOhvE",
        "pycharm": {
          "name": "#%% md\n"
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "source": [
        "# Initialize Retriever\n",
        "from haystack.nodes import ElasticsearchRetriever\n",
        "retriever = ElasticsearchRetriever(document_store=document_store)\n",
        "# Alternative: Evaluate dense Retrievers (DPR and SentenceTransformers)\n",
        "# Dense Passage Retrieval uses a separate transformer based encoder for query and document each\n",
        "# SentenceTransformers have a single encoder for both\n",
        "# Please make sure the \"embedding_dim\" parameter in the DocumentStore above matches the output dimension of you models!\n",
        "# Please also take care that the PreProcessor splits your files into chunks that can be completely converted with\n",
        "#        the max_seq_len limitations of Transformers\n",
        "# The SentenceTransformer model \"all-mpnet-base-v2\" generelly works well on any kind of english text.\n",
        "# For more information check out the documentation at: https://www.sbert.net/docs/pretrained_models.html\n",
        "# from haystack.retriever import DensePassageRetriever, EmbeddingRetriever\n",
        "# retriever = DensePassageRetriever(document_store=document_store,\n",
        "#                                   query_embedding_model=\"facebook/dpr-question_encoder-single-nq-base\",\n",
        "#                                   passage_embedding_model=\"facebook/dpr-ctx_encoder-single-nq-base\",\n",
        "#                                   use_gpu=True,\n",
        "#                                   max_seq_len_passage=256,\n",
        "#                                   embed_title=True)\n",
        "# retriever = EmbeddingRetriever(document_store=document_store, model_format=\"sentence_transformers\",\n",
        "#                                embedding_model=\"all-mpnet-base-v2\")\n",
        "# document_store.update_embeddings(retriever, index=doc_index)"
      ],
      "outputs": [],
      "metadata": {
        "id": "JkhaPMIJOhvF",
        "pycharm": {
          "name": "#%%\n"
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "source": [
        "# Initialize Reader\n",
        "from haystack.nodes import FARMReader\n",
        "\n",
        "reader = FARMReader(\"deepset/roberta-base-squad2\", top_k=4, return_no_answer=True)\n"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "118aedffeecd4f109ae04a4561baeb08",
              "version_minor": 0,
              "version_major": 2
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          "metadata": {}
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          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "Some weights of the model checkpoint at deepset/roberta-base-squad2 were not used when initializing RobertaModel: ['qa_outputs.bias', 'qa_outputs.weight']\n",
            "- This IS expected if you are initializing RobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of RobertaModel were not initialized from the model checkpoint at deepset/roberta-base-squad2 and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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          "text": [
            "ML Logging is turned off. No parameters, metrics or artifacts will be logged to MLFlow.\n"
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        },
        "id": "cW3Ypn_gOhvK",
        "pycharm": {
          "name": "#%%\n"
        },
        "outputId": "4b5feff7-ae9f-4cd8-de1e-944f0eb66f66"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "source": [
        "from haystack.nodes import EvalAnswers, EvalDocuments\n",
        "\n",
        "# Here we initialize the nodes that perform evaluation\n",
        "eval_retriever = EvalDocuments()\n",
        "eval_reader = EvalAnswers(sas_model=\"sentence-transformers/paraphrase-multilingual-mpnet-base-v2\")"
      ],
      "outputs": [],
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "NRq01w8iU7tN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluation of Retriever\n",
        "Here we evaluate only the retriever, based on whether the gold_label document is retrieved."
      ],
      "metadata": {
        "id": "qwkBgzh5OhvR",
        "pycharm": {
          "name": "#%% md\n"
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "source": [
        "## Evaluate Retriever on its own\n",
        "retriever_eval_results = retriever.eval(top_k=20, label_index=label_index, doc_index=doc_index)\n",
        "## Retriever Recall is the proportion of questions for which the correct document containing the answer is\n",
        "## among the correct documents\n",
        "print(\"Retriever Recall:\", retriever_eval_results[\"recall\"])\n",
        "## Retriever Mean Avg Precision rewards retrievers that give relevant documents a higher rank\n",
        "print(\"Retriever Mean Avg Precision:\", retriever_eval_results[\"map\"])"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 771/771 [00:07<00:00, 106.27it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Retriever Recall: 0.32814526588845655\n",
            "Retriever Mean Avg Precision: 0.09174006526330329\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YzvLhnx3OhvS",
        "pycharm": {
          "name": "#%%\n"
        },
        "outputId": "ca5bbc09-6407-4d57-f234-f14ee26aca96"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluation of Reader\n",
        "Here we evaluate only the reader in a closed domain fashion i.e. the reader is given one query\n",
        "and one document and metrics are calculated on whether the right position in this text is selected by\n",
        "the model as the answer span (i.e. SQuAD style)"
      ],
      "metadata": {
        "id": "fjZRnB6bOhvW",
        "pycharm": {
          "name": "#%% md\n"
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "source": [
        "# Evaluate Reader on its own\n",
        "reader_eval_results = reader.eval(document_store=document_store, device=devices[0], label_index=label_index, doc_index=doc_index)\n",
        "# Evaluation of Reader can also be done directly on a SQuAD-formatted file without passing the data to Elasticsearch\n",
        "#reader_eval_results = reader.eval_on_file(\"../data/nq\", \"nq_dev_subset_v2.json\", device=device)\n",
        "\n",
        "## Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer\n",
        "print(\"Reader Top-N-Accuracy:\", reader_eval_results[\"top_n_accuracy\"])\n",
        "## Reader Exact Match is the proportion of questions where the predicted answer is exactly the same as the correct answer\n",
        "print(\"Reader Exact Match:\", reader_eval_results[\"EM\"])\n",
        "## Reader F1-Score is the average overlap between the predicted answers and the correct answers\n",
        "print(\"Reader F1-Score:\", reader_eval_results[\"f1\"])"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rEvaluating:   0%|          | 0/32 [00:00<?, ?it/s]/usr/local/lib/python3.7/dist-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.\n",
            "To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at  /pytorch/aten/src/ATen/native/BinaryOps.cpp:467.)\n",
            "  return torch.floor_divide(self, other)\n",
            "Evaluating: 100%|██████████| 32/32 [00:45<00:00,  1.41s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reader Top-N-Accuracy: 98.94875164257556\n",
            "Reader Exact Match: 84.62549277266754\n",
            "Reader F1-Score: 85.16284899931244\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lgsgf4KaOhvY",
        "pycharm": {
          "name": "#%%\n"
        },
        "outputId": "aac2042b-8638-48ec-d4da-5e8923ebdebc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluation of Retriever and Reader (Open Domain)\n",
        "Here we evaluate retriever and reader in open domain fashion i.e. a document is considered\n",
        "correctly retrieved if it contains the answer string within it. The reader is evaluated based purely on the\n",
        "predicted string, regardless of which document this came from and the position of the extracted span."
      ],
      "metadata": {
        "id": "7i84KXONOhvc",
        "pycharm": {
          "name": "#%% md\n"
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "source": [
        "from haystack import Pipeline\n",
        "\n",
        "# Here is the pipeline definition\n",
        "p = Pipeline()\n",
        "p.add_node(component=retriever, name=\"Retriever\", inputs=[\"Query\"])\n",
        "p.add_node(component=eval_retriever, name=\"EvalRetriever\", inputs=[\"Retriever\"])\n",
        "p.add_node(component=reader, name=\"Reader\", inputs=[\"EvalRetriever\"])\n",
        "p.add_node(component=eval_reader, name=\"EvalReader\", inputs=[\"Reader\"])\n",
        "results = []"
      ],
      "outputs": [],
      "metadata": {
        "id": "yLpMHAexOhvd",
        "pycharm": {
          "name": "#%%\n"
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "source": [
        "# This is how to run the pipeline\n",
        "for l in labels:\n",
        "    res = p.run(\n",
        "        query=l.query,\n",
        "        labels=l,\n",
        "        params={\"index\": doc_index, \"Retriever\": {\"top_k\": 10}, \"Reader\": {\"top_k\": 5}},\n",
        "    )\n",
        "    results.append(res)"
      ],
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        {
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            "d6125036c6cd4b04badcda02419be090",
            "a28f716145f046fcb101ae6eb5d55e2f",
            "e65ae480d2d04ab48c8624eb1fca45d8",
            "2bfc05c7ef514354bfb41fe6290dbf33",
            "51f5c4ae62124c2eb09cd655d50a042c",
            "76a9a9ff30384c63bbafac8dad7e6b6f",
            "9f23dc687bb54d8ca102ebd1f35b45ee",
            "54a4a9ebf2b24542a97157643fc8760e",
            "b0fda0f7f1694c958fabb866c8837b07",
            "bcca8127f74f4e36813a902020d9cb4e"
          ]
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "source": [
        "# When we have run evaluation using the pipeline, we can print the results\n",
        "n_queries = len(labels)\n",
        "eval_retriever.print()\n",
        "print()\n",
        "retriever.print_time()\n",
        "print()\n",
        "eval_reader.print(mode=\"reader\")\n",
        "print()\n",
        "reader.print_time()\n",
        "print()\n",
        "eval_reader.print(mode=\"pipeline\")"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EvalRetriever\n",
            "-----------------\n",
            "has_answer recall@10: 0.9200 (23/25)\n",
            "no_answer recall@10:  1.00 (25/25) (no_answer samples are always treated as correctly retrieved)\n",
            "has_answer mean_reciprocal_rank@10: 0.6110\n",
            "no_answer mean_reciprocal_rank@10:  1.0000 (no_answer samples are always treated as correctly retrieved at rank 1)\n",
            "recall@10: 0.9600 (48 / 50)\n",
            "mean_reciprocal_rank@10: 0.8055\n",
            "\n",
            "Retriever (Speed)\n",
            "---------------\n",
            "No indexing performed via Retriever.run()\n",
            "Queries Performed: 50\n",
            "Query time: 0.5041560229994957s\n",
            "0.010083120459989913 seconds per query\n",
            "\n",
            "Reader\n",
            "-----------------\n",
            "has answer queries: 23\n",
            "top 1 EM: 0.2609\n",
            "top k EM: 0.5217\n",
            "top 1 F1: 0.3364\n",
            "top k F1: 0.6180\n",
            "top 1 SAS: 0.5375\n",
            "top k SAS: 0.7885\n",
            "\n",
            "no_answer queries: 25\n",
            "top 1 no_answer accuracy: 0.0000\n",
            "\n",
            "Reader (Speed)\n",
            "---------------\n",
            "Queries Performed: 50\n",
            "Query time: 80.12184924100029s\n",
            "1.6024369848200057 seconds per query\n",
            "\n",
            "Pipeline\n",
            "-----------------\n",
            "queries: 50\n",
            "top 1 EM: 0.1200\n",
            "top k EM: 0.7400\n",
            "top 1 F1: 0.1548\n",
            "top k F1: 0.7843\n",
            "top 1 SAS: 0.2473\n",
            "top k SAS: 0.8627\n",
            "(top k results are likely inflated since the Reader always returns a no_answer prediction in its top k)\n"
          ]
        }
      ],
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "WMj1gw2NU7ta",
        "outputId": "1b539dba-18c8-41a9-9bb2-b519be1061fb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## About us\n",
        "\n",
        "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
        "\n",
        "We bring NLP to the industry via open source!  \n",
        "Our focus: Industry specific language models & large scale QA systems.  \n",
        "  \n",
        "Some of our other work: \n",
        "- [German BERT](https://deepset.ai/german-bert)\n",
        "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
        "- [FARM](https://github.com/deepset-ai/FARM)\n",
        "\n",
        "Get in touch:\n",
        "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
        "\n",
        "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
      ],
      "metadata": {
        "collapsed": false,
        "id": "8QJ68G12U7tb"
      }
    }
  ]
}
